Cryogenic $\text{In}_{0.8} \text{Ga}_{0.2} \text{As}$ Quantum-Well High-Electron Mobility Transistors from Lowpower Quantum Computing to Tera-Hz Applications
Bibliographic record
Abstract
We present cryogenic$\text{In}_{0.8} \text{Ga}_{0.2} \text{As}$QW HEMTs with a gate length$\left(L_{g}\right)$of 35 nm, achieving a record combination of low-power and high-frequency performance. A meticulous modeling of source resistance$\left(R_{s}\right)$incorporating ballistic channel resistance ($\left.R_{\text {ball }}\right)$- provides key insights for advancing low-power quantum computing and terahertz (THz) applications. At 4 K, the fabricated device exhibits exceptional performance metrics, including a minimum subthreshold-swing$\left(S_{\min }\right)$of$4.41 \text{mV} / \text{dec}$., a$g_{m_{-} \max }$of$2.49 \text{mS} / \mu \mathrm{m}$, and the highest record$f_{T}$of 813 GHz, with an average gain-bandwidth product ($f_{\text {avg }}$) of 810 GHz. These results stem from a tightly controlled gate-recess process, minimizing the side length ($L_{\text {side }}$) to below 20 nm. Delay-time analysis indicates further THz performance can be achieved by scaled$L_{g}$below 20 nm and reducing fringing gate capacitance ($C_{\mathrm{g}\_\text{fringe }}$) by 20 %. This work demonstrates the potential of cryogenic$\text{In}_{0.8} \text{Ga}_{0.2}$As QW HEMTs to revolutionize quantum computing and THz electronics
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".